In recent years, deep neural networks have emerged as a promising alternative method for human motion generation.
These approaches are often capable of generating diverse motions, but often fall short in capturing the physical laws governing human movement or rely on difficult-to-train reinforcement learning solutions [1]}, [2]}.
Generating human motion conditioned on various inputs—e.g. joystick control [1]}, class-conditioning [4]}, [5]}, text-to-motion [6]}, [7]}, seed motions [8]}, [9]}, [10]}, [11]}—is also an active area of study.
